Getting started

Workflow

For the coursework each week (exercises, lab, and homework) you will start with a GitHub repo that I created for you which contains all the files you will need. I will share the link to your weekly repo on the Weekly Content blackboard page, on Monday before class. For week 1 the repo will be called week-1-yourlastname. This week during class you cloned (copied) this repo into RStudioCloud and worked on the exercises and lab. For ths homework, you will build upon the hw-01-earthquakes-starter Rmd file for your homework assignment.

The first step is always to bring these files into RStudio so that you can edit them, run them, view your results, and interpret them. Since you already are looking at this homework 1 RMarkdown file, you have already accomplished all of this! However let’s recap these steps because being able to do this is part of the first week’s lesson!

First we need to copy the GItHub repo to RStudioCloud: This action is called cloning.

Then you work in RStudio on the data analysis, making commits along the way (snapshots of your changes) and finally push all your work back to GitHub.

The next few steps will remind you of the process of getting information of the repo to be cloned, cloning your repo in a new RStudio Cloud project, and getting started with the analysis.

Hello RStudio!

Let’s remind ourselves of the RStudio working environment.

RStudio is comprised of four panes.

• On the bottom left is the Console, this is where you can write code that will be evaluated. Try typing 2 + 2 here and hit enter, what do you get?
• On the bottom right is the Files pane, as well as other panes that will come handy as we start our analysis.
• If you click on a file, it will open in the editor, on the top left pane.
• Finally, the top right pane shows your Environment. If you define a variable it would show up there. Try typing x <- 2 in the Console and hit enter, what do you get in the Environment pane? Importantly, this pane is also where the Git interface lives. We will be using that regularly throughout this assignment.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

⊕The top portion of your R Markdown file (between the three dashed lines) is called YAML. It stands for “YAML Ain’t Markup Language”. It is a human friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.

Packages

R is an open-source language, and developers contribute functionality to R via packages. In this assignment we will use the following packages:

• tidyverse: a collection of packages for doing data analysis in a “tidy” way
• openintro: a package that contains the datasets from OpenIntro resources

We use the library() function to load packages. In your R Markdown document you should see an R chunk labelled load-packages which has the necessary code for loading both packages. You should also load these packages in your Console, which you can do by sending the code to your Console by clicking on the Run Current Chunk icon (green arrow pointing right icon).

load-packages chunk to run

Note that these packages are also get loaded in your R Markdown environment when you Knit your R Markdown document.

Earthquake Data

The World Almanac and Book of Facts (2011) has compiled records of notable earthquakes in the 20th century (1900-1999). For each earthquake, we have information on the date, strength, location, and death toll. The data used in this exercise can be found in the openintro package, and it’s called earthquakes. Since the dataset is distributed with the package, we don’t need to load it separately; it becomes available to us when we load the package.

You can view the dataset as a spreadsheet using the View() function. Note that you should not put this function in your R Markdown document, but instead type it directly in the Console, as it pops open a new window (and the concept of popping open a window in a static document doesn’t really make sense…). When you run this in the console, you’ll see the following data viewer window pop up.

View(earthquakes)

You can find out more about the dataset by inspecting its documentation (which contains a data dictionary, name of each variable and its description), which you can access by running ??earthquakes in the Console or using the Help menu in RStudio to search for earthquakes.

Exercises

1. According to the data dictionary, how many earthquakes are included in this dataset?

🧶 ✅ ⬆️ Write your answer in your R Markdown document under Exercise 1, knit the document, commit your changes with a commit message that says “Completed Exercise 1”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

2. Again, according to the data dictionary, how many variables do we have for each earthquake?

🧶 ✅ ⬆️ Write your answer in your R Markdown document under Exercise 1, knit the document, commit your changes with a commit message that says “Completed Exercise 2”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

3. Which countries experienced the most notable earthquakes in the 20th century? To do this you will need to count the frequencies of region and display the results in descending order of frequency so that you can easily see the top three most earthquake-prone regions. The following code does exactly that.

⊕The two lines of code can be read as “Start with the earthquakes data frame, and then count the regions, and display the results sorted in descending order. The”and then” in the previous sentence maps to %>%, the pipe operator, which takes what comes before it and plugs it in as the first argument of the function that comes after it.

earthquakes %>%
  count(region, sort = TRUE)

🧶 ✅ ⬆️ Write your answer in your R Markdown document under Exercise 3. In this exercise you will not only provide a written answer but also include some code and output. You should insert the code in the code chunk provided for you, knit the document to see the output, and then write your narrative for the answer based on the output of this function, and knit again to see your narrative, code, and output in the resulting document. Then, commit your changes with a commit message that says “Completed Exercise 3”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Let’s also look to see what the most common areas are, which provides a more specific location like a city. For this we need to first group_by() the area, and then do the same counting we did before.

⊕It looks like eastern Turkey was hit by several notable earthquakes…

earthquakes %>% 
  group_by(area) %>%
  count(region, sort = TRUE)

## # A tibble: 106 × 3
## # Groups:   area [89]
##    area          region            n
##    <chr>         <chr>         <int>
##  1 East          Turkey            5
##  2 South         Iran              3
##  3 Yunnan        China             3
##  4 Gansu         China             2
##  5 Guangdong     China             2
##  6 Managua       Nicaragua         2
##  7 North         Iran              2
##  8 Northeast     Iran              2
##  9 Northwest     Iran              2
## 10 San Francisco United States     2
## # … with 96 more rows

We can also look to see what the most common months for earthquake are. We need month this time…

earthquakes %>% 
  count(month, sort = TRUE)

## # A tibble: 12 × 2
##    month         n
##    <chr>     <int>
##  1 March        17
##  2 May          14
##  3 August       12
##  4 December     12
##  5 January      11
##  6 July         10
##  7 February      9
##  8 September     9
##  9 June          8
## 10 October       8
## 11 April         7
## 12 November      6

What if we just wanted to look at the top three months for earthquakes? To do this, we can use the slice_max() function. The first argument in the function is the variable we want to select the highest values of, which is n. The second argument is the number of rows to select, which is n = 3 for the top 3. It may be a bit confusing that both of these are n, but this is because we already have a variable called n in the data frame.

earthquakes %>% 
  count(month, sort = TRUE) %>% 
  slice_max(n, n = 3) 

## # A tibble: 4 × 2
##   month        n
##   <chr>    <int>
## 1 March       17
## 2 May         14
## 3 August      12
## 4 December    12

Notice that R returns 4 rows because there are two months that had an equal number of earthquakes. Remember this is because slice_max() returns the top three values of n, which could include multiple months for one value.

This time let’s search for the top 5 most common earthquake intensities using the count() and slice_max() functions. Remember earthquake magnitude is expressed on the Richter scale so we can use the variable richter in this dataset.

earthquakes %>% 
  count(richter, sort = TRUE) %>% 
  slice_max(n, n = 5) 

## # A tibble: 6 × 2
##   richter     n
##     <dbl> <int>
## 1     7.3    13
## 2     6.8    11
## 3     7.5    10
## 4     7       9
## 5     6.9     7
## 6     7.1     7

1. Based on the previous output we can easily identify the most common intensities for notable earthquakes in the 20th century but the output is sorted by n (the frequencies) as opposed to being organized by richter. Build on the pipeline to arrange the results so that they’re arranged by richter. This means you will need to add one more step to the pipeline using arrange().

🧶 ✅ ⬆️ Write your answer in your R Markdown document under Exercise 4. In this exercise you’re asked to complete the code provided for you. You should insert the code in the code chunk provided for you, knit the document to see the output, and then write your narrative for the answer based on the output of this function, and knit again to see your narrative, code, and output in the resulting document. Then, commit your changes with a commit message that says “Completed Exercise 4”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

The following visualization plots the richter magnitude of earthquakes versus the year, showing a time series plot. The straight red line on the plot is the average magnitude over the century mean(earthquakes$richter).

1. Does the relationship between the two variables (earthquake magnitude vs. year of the 20th century) seem to be positive, negative, or no relationship? What does this mean in context of the data?

The next visualization plots the death toll (deaths) of earthquakes versus the year, showing another time series plot.

1. Looking at this and the previous visualization, do you think it is likely that the death toll can be predicted simply by the magnitude of the earthquake?

The final visualization plots the death toll (deaths) of earthquakes versus the richter magnitude, showing a scatter plot.

1. Does this final visualization give you more or less confidence in your last response about the relationship between earthquake magnitude and death toll?

🧶 ✅ ⬆️ Now is a good time to commit and push your changes to GitHub with an appropriate commit message. Commit and push all changed files so that your Git pane is cleared up afterwards. Make sure that your last push to the repo comes before the deadline. You should confirm that what you committed and pushed are indeed in your repo that we will see by visiting your repo on GitHub.